Cloud based evaluation of databases for stock market data.

About fifty years ago, the world's first fully automated system for trading securities was introduced by Instinet in the US. Since then the world of trading has been revolutionised by the introduction of electronic markets and automatic order execution. Nowadays, financial institutions exploit the associated flow of daily data using more and more advanced analytics to gain valuable insight on the markets and inform their investment decisions. In particular, time series of Open High Low Close prices and Volume data are of special interest as they allow identifying trading patterns useful for forecasting both stock prices and volumes. Traditionally, relational databases have been used to store this data; however, the ever-growing volume of this data, the adoption of the hybrid cloud model, and the availability of novel non-relational databases which claim to be more scalable and fault tolerant raise the question whether relational databases are still the most appropriate. In this study, we define a set of criteria to evaluate performance of a variety of databases on a hybrid cloud environment. There, we conduct experiments using standard and custom workloads. Results show that migration to a MongoDB database would be most beneficial in terms of cost, storage space, and throughput. In addition, organisations wishing to take advantage of autoscaling and the maintenance power of the cloud should opt for a cloud native solution.

In silico design of bioisosteric modifications of drugs for the treatment of diabetes.

Aim: To identify virtual bioisosteric replacements of two GPR40 agonists. Materials & methods: Bioinformatic docking of candidate molecules featuring a wide range of carboxylic acid bioisosteres into complex with GPR40 was performed using TAK-875 and GW9508 templates. Results: This study suggests that 2,6-difluorophenol and squaric acid motifs are the preferred bioisosteric groups for conferring GPR40 affinity. Conclusion: This study suggests that compounds 10 and 20 are worthy synthetic targets.

Game theory to enhance stock management of Personal Protective Equipment (PPE) during the COVID-19 outbreak.

Since the outbreak of the COVID-19 pandemic, many healthcare facilities have suffered from shortages in medical resources, particularly in Personal Protective Equipment (PPE). In this paper, we propose a game-theoretic approach to schedule PPE orders among healthcare facilities. In this PPE game, each independent healthcare facility optimises its own storage utilisation in order to keep its PPE cost at a minimum. Such a model can reduce peak demand considerably when applied to a variable PPE consumption profile. Experiments conducted for NHS England regions using actual data confirm that the challenge of securing PPE supply during disasters such as COVID-19 can be eased if proper stock management procedures are adopted. These procedures can include early stockpiling, increasing storage capacities and implementing measures that can prolong the time period between successive infection waves, such as social distancing measures. Simulation results suggest that the provision of PPE dedicated storage space can be a viable solution to avoid straining PPE supply chains in case a second wave of COVID-19 infections occurs.

Enhancing fragment-based protein structure prediction by customising fragment cardinality according to local secondary structure.

BACKGROUND: Whenever suitable template structures are not available, usage of fragment-based protein structure prediction becomes the only practical alternative as pure ab initio techniques require massive computational resources even for very small proteins. However, inaccuracy of their energy functions and their stochastic nature imposes generation of a large number of decoys to explore adequately the solution space, limiting their usage to small proteins. Taking advantage of the uneven complexity of the sequence-structure relationship of short fragments, we adjusted the fragment insertion process by customising the number of available fragment templates according to the expected complexity of the predicted local secondary structure. Whereas the number of fragments is kept to its default value for coil regions, important and dramatic reductions are proposed for beta sheet and alpha helical regions, respectively. RESULTS: The evaluation of our fragment selection approach was conducted using an enhanced version of the popular Rosetta fragment-based protein structure prediction tool. It was modified so that the number of fragment candidates used in Rosetta could be adjusted based on the local secondary structure. Compared to Rosetta's standard predictions, our strategy delivered improved first models, + 24% and + 6% in terms of GDT, when using 2000 and 20,000 decoys, respectively, while reducing significantly the number of fragment candidates. Furthermore, our enhanced version of Rosetta is able to deliver with 2000 decoys a performance equivalent to that produced by standard Rosetta while using 20,000 decoys. We hypothesise that, as the fragment insertion process focuses on the most challenging regions, such as coils, fewer decoys are needed to explore satisfactorily conformation spaces. CONCLUSIONS: Taking advantage of the high accuracy of sequence-based secondary structure predictions, we showed the value of that information to customise the number of candidates used during the fragment insertion process of fragment-based protein structure prediction. Experimentations conducted using standard Rosetta showed that, when using the recommended number of decoys, i.e. 20,000, our strategy produces better results. Alternatively, similar results can be achieved using only 2000 decoys. Consequently, we recommend the adoption of this strategy to either improve significantly model quality or reduce processing times by a factor 10.

Deep phosphoproteome analysis of Schistosoma mansoni leads development of a kinomic array that highlights sex-biased differences in adult worm protein phosphorylation.

Although helminth parasites cause enormous suffering worldwide we know little of how protein phosphorylation, one of the most important post-translational modifications used for molecular signalling, regulates their homeostasis and function. This is particularly the case for schistosomes. Herein, we report a deep phosphoproteome exploration of adult Schistosoma mansoni, providing one of the richest phosphoprotein resources for any parasite so far, and employ the data to build the first parasite-specific kinomic array. Complementary phosphopeptide enrichment strategies were used to detect 15,844 unique phosphopeptides mapping to 3,176 proteins. The phosphoproteins were predicted to be involved in a wide range of biological processes and phosphoprotein interactome analysis revealed 55 highly interconnected clusters including those enriched with ribosome, proteasome, phagosome, spliceosome, glycolysis, and signalling proteins. 93 distinct phosphorylation motifs were identified, with 67 providing a 'footprint' of protein kinase activity; CaMKII, PKA and CK1/2 were highly represented supporting their central importance to schistosome function. Within the kinome, 808 phosphorylation sites were matched to 136 protein kinases, and 68 sites within 37 activation loops were discovered. Analysis of putative protein kinase-phosphoprotein interactions revealed canonical networks but also novel interactions between signalling partners. Kinomic array analysis of male and female adult worm extracts revealed high phosphorylation of transformation:transcription domain associated protein by both sexes, and CDK and AMPK peptides by females. Moreover, eight peptides including protein phosphatase 2C gamma, Akt, Rho2 GTPase, SmTK4, and the insulin receptor were more highly phosphorylated by female extracts, highlighting their possible importance to female worm function. We envision that these findings, tools and methodology will help drive new research into the functional biology of schistosomes and other helminth parasites, and support efforts to develop new therapeutics for their control.

Dynamic Spatio-Temporal Bag of Expressions (D-STBoE) Model for Human Action Recognition.

Human action recognition (HAR) has emerged as a core research domain for video understanding and analysis, thus attracting many researchers. Although significant results have been achieved in simple scenarios, HAR is still a challenging task due to issues associated with view independence, occlusion and inter-class variation observed in realistic scenarios. In previous research efforts, the classical bag of visual words approach along with its variations has been widely used. In this paper, we propose a Dynamic Spatio-Temporal Bag of Expressions (D-STBoE) model for human action recognition without compromising the strengths of the classical bag of visual words approach. Expressions are formed based on the density of a spatio-temporal cube of a visual word. To handle inter-class variation, we use class-specific visual word representation for visual expression generation. In contrast to the Bag of Expressions (BoE) model, the formation of visual expressions is based on the density of spatio-temporal cubes built around each visual word, as constructing neighborhoods with a fixed number of neighbors could include non-relevant information making a visual expression less discriminative in scenarios with occlusion and changing viewpoints. Thus, the proposed approach makes the model more robust to occlusion and changing viewpoint challenges present in realistic scenarios. Furthermore, we train a multi-class Support Vector Machine (SVM) for classifying bag of expressions into action classes. Comprehensive experiments on four publicly available datasets: KTH, UCF Sports, UCF11 and UCF50 show that the proposed model outperforms existing state-of-the-art human action recognition methods in term of accuracy to 99.21%, 98.60%, 96.94 and 94.10%, respectively.

Coil conversion to ß-strand induced by dimerization.

Most molecular processes in living organisms rely on protein-protein interactions, many of which are mediated by ß-sheet interfaces; this study investigates the formation of ß-sheet interfaces through the conversion of coils into ß-strands. Following an exhaustive search in the Protein Data Bank, the corresponding structural dimorphic fragments were extracted, characterized, and analyzed. Their short strand lengths and specific amino acid profiles indicate that dimorphic ß-strand interfaces are likely to be less stable than standard ones and could even convert to coil interfaces if their environment changes. Moreover, the construction of a simple classifier able to discriminate between the sequences of dimorphic and standard ß-strand interfaces suggests that the nature of those dimorphic sequences could be predicted, providing a novel means of identifying proteins capable of forming dimers.

Identifying human diamine sensors for death related putrescine and cadaverine molecules.

Pungent chemical compounds originating from decaying tissue are strong drivers of animal behavior. Two of the best-characterized death smell components are putrescine (PUT) and cadaverine (CAD), foul-smelling molecules produced by decarboxylation of amino acids during decomposition. These volatile polyamines act as 'necromones', triggering avoidance or attractive responses, which are fundamental for the survival of a wide range of species. The few studies that have attempted to identify the cognate receptors for these molecules have suggested the involvement of the seven-helix trace amine-associated receptors (TAARs), localized in the olfactory epithelium. However, very little is known about the precise chemosensory receptors that sense these compounds in the majority of organisms and the molecular basis of their interactions. In this work, we have used computational strategies to characterize the binding between PUT and CAD with the TAAR6 and TAAR8 human receptors. Sequence analysis, homology modeling, docking and molecular dynamics studies suggest a tandem of negatively charged aspartates in the binding pocket of these receptors which are likely to be involved in the recognition of these small biogenic diamines.

Reduced Fragment Diversity for Alpha and Alpha-Beta Protein Structure Prediction using Rosetta.

Protein structure prediction is considered a main challenge in computational biology. The biannual international competition, Critical Assessment of protein Structure Prediction (CASP), has shown in its eleventh experiment that free modelling target predictions are still beyond reliable accuracy, therefore, much effort should be made to improve ab initio methods. Arguably, Rosetta is considered as the most competitive method when it comes to targets with no homologues. Relying on fragments of length 9 and 3 from known structures, Rosetta creates putative structures by assembling candidate fragments. Generally, the structure with the lowest energy score, also known as first model, is chosen to be the "predicted one". A thorough study has been conducted on the role and diversity of 3-mers involved in Rosetta's model "refinement" phase. Usage of the standard number of 3-mers - i.e. 200 - has been shown to degrade alpha and alpha-beta protein conformations initially achieved by assembling 9-mers. Therefore, a new prediction pipeline is proposed for Rosetta where the "refinement" phase is customised according to a target's structural class prediction. Over 8% improvement in terms of first model structure accuracy is reported for alpha and alpha-beta classes when decreasing the number of 3- mers.

Inversion of Correia repeat enclosed elements in Neisseria gonorrhoeae.

Neisseria gonorrhoeae is capable of causing gonorrhoea and more complex diseases in the human host. Within the gonococcal genome are over 100 copies of the insertion sequence-like Correia repeat enclosed element (CREE), which has been predicted to be mobile within the neisserial genomes. Although there is evidence of ancestral movement of these elements, no previous study has provided evidence for current mobilization. CREE has the ability to alter gene expression and regulation in many ways: by insertional mutagenesis, by introducing promoter elements, by generating mRNA processing sites and by association with non-coding RNAs. Previous studies have compared the genomic locations of CREEs in the Neisseria spp., demonstrating that otherwise identical regions have either the element or the target TA insertion site. In this study, we report for the first time, to our knowledge, movement of CREEs, through inversion of the element at its chromosomal location. Analysis of Ion Torrent generated genome sequence data from N. gonorrhoeae strain NCCP11945 passaged for 8 weeks in the laboratory under standard conditions and stress conditions revealed a total of 37 inversions: 24 were exclusively seen in the stressed sample, 7 were seen in the control sample and the remaining 3 were seen in both samples. These inversions have the capability to alter gene expression in N. gonorrhoeae through the previously determined activities of the sequence features of these elements, potentially resulting in reversible phase-variable gene expression.

Correia Repeat Enclosed Elements and Non-Coding RNAs in the Neisseria Species.

Neisseria gonorrhoeae is capable of causing gonorrhoea and more complex diseases in the human host. Neisseria meningitidis is a closely related pathogen that shares many of the same genomic features and virulence factors, but causes the life threatening diseases meningococcal meningitis and septicaemia. The importance of non-coding RNAs in gene regulation has become increasingly evident having been demonstrated to be involved in regulons responsible for iron acquisition, antigenic variation, and virulence. Neisseria spp. contain an IS-like element, the Correia Repeat Enclosed Element, which has been predicted to be mobile within the genomes or to have been in the past. This repeat, present in over 100 copies in the genome, has the ability to alter gene expression and regulation in several ways. We reveal here that Correia Repeat Enclosed Elements tend to be near non-coding RNAs in the Neisseria spp., especially N. gonorrhoeae. These results suggest that Correia Repeat Enclosed Elements may have disrupted ancestral regulatory networks not just through their influence on regulatory proteins but also for non-coding RNAs.

Recognition of Activities of Daily Living with Egocentric Vision: A Review.

Video-based recognition of activities of daily living (ADLs) is being used in ambient assisted living systems in order to support the independent living of older people. However, current systems based on cameras located in the environment present a number of problems, such as occlusions and a limited field of view. Recently, wearable cameras have begun to be exploited. This paper presents a review of the state of the art of egocentric vision systems for the recognition of ADLs following a hierarchical structure: motion, action and activity levels, where each level provides higher semantic information and involves a longer time frame. The current egocentric vision literature suggests that ADLs recognition is mainly driven by the objects present in the scene, especially those associated with specific tasks. However, although object-based approaches have proven popular, object recognition remains a challenge due to the intra-class variations found in unconstrained scenarios. As a consequence, the performance of current systems is far from satisfactory.

Nutrigenomics 2.0: The Need for Ongoing and Independent Evaluation and Synthesis of Commercial Nutrigenomics Tests' Scientific Knowledge Base for Responsible Innovation.

Nutrigenomics is an important strand of precision medicine that examines the bidirectional interactions of the genome and nutritional exposures, and attendant health and disease outcomes. This perspectives article presents the new concept of "Nutrigenomics 2.0," so as to cultivate and catalyze the next generation research and funding priorities for responsible and sustainable knowledge-based innovations. We further contextualize our recent study of the 38 genes included in commercially available nutrigenomics tests, and offer additional context in relation to the 2014 American Academy of Nutrition and Dietetics position. Finally, we make a call in the best interest of the nutrigenomics science community, governments, global society, and commercial nutrigenomics test providers that new evidence evaluation and synthesis platforms are created concerning nutrigenomics tests before they become commercially available. The proposed assessment and synthesis of nutrigenomics data should be carried out on an ongoing dynamic basis with periodic intervals and/or when there is a specific demand for evidence synthesis, and importantly, in ways that are transparent where conflict of interests are disclosed fully by the involved parties, be they scientists, industry, governments, citizens, social scientists, or ethicists. We submit that this will cultivate responsible innovation, and business models that are sustainable, robust, and stand the test of time and context.

Progress and challenges in predicting protein interfaces.

The majority of biological processes are mediated via protein-protein interactions. Determination of residues participating in such interactions improves our understanding of molecular mechanisms and facilitates the development of therapeutics. Experimental approaches to identifying interacting residues, such as mutagenesis, are costly and time-consuming and thus, computational methods for this purpose could streamline conventional pipelines. Here we review the field of computational protein interface prediction. We make a distinction between methods which address proteins in general and those targeted at antibodies, owing to the radically different binding mechanism of antibodies. We organize the multitude of currently available methods hierarchically based on required input and prediction principles to provide an overview of the field.

Meta-Analysis of Genes in Commercially Available Nutrigenomic Tests Denotes Lack of Association with Dietary Intake and Nutrient-Related Pathologies.

Nutrigenomics is an emerging discipline that aims to investigate how individual genetic composition correlates with dietary intake, as well as how nutrition influences gene expression. Herein, the fundamental question relates to the value of nutrigenomics testing on the basis of the currently available scientific evidence. A thorough literature search has been conducted in PubMed scientific literature database for nutrigenomics research studies on 38 genes included in nutrigenomics tests provided by various private genetic testing laboratories. Data were subsequently meta-analyzed to identify possible associations between the genes of interest and dietary intake and/or nutrient-related pathologies. Data analysis occurred according to four different models due to data sparsity and inconsistency. Data from 524,592 individuals (361,153 cases and 163,439 controls) in a total of 1,170 entries were obtained. Conflicting findings indicated that there was a great incompatibility regarding the associations (or their absence) identified. No specific--and statistically significant-association was identified for any of the 38 genes of interest. In those cases, where a weak association was demonstrated, evidence was based on a limited number of studies. As solid scientific evidence is currently lacking, commercially available nutrigenomics tests cannot be presently recommended. Notwithstanding, the need for a thorough and continuous nutrigenomics research is evident as it is a highly promising tool towards precision medicine.

Customised fragments libraries for protein structure prediction based on structural class annotations.

BACKGROUND: Since experimental techniques are time and cost consuming, in silico protein structure prediction is essential to produce conformations of protein targets. When homologous structures are not available, fragment-based protein structure prediction has become the approach of choice. However, it still has many issues including poor performance when targets' lengths are above 100 residues, excessive running times and sub-optimal energy functions. Taking advantage of the reliable performance of structural class prediction software, we propose to address some of the limitations of fragment-based methods by integrating structural constraints in their fragment selection process. RESULTS: Using Rosetta, a state-of-the-art fragment-based protein structure prediction package, we evaluated our proposed pipeline on 70 former CASP targets containing up to 150 amino acids. Using either CATH or SCOP-based structural class annotations, enhancement of structure prediction performance is highly significant in terms of both GDT_TS (at least +2.6, p-values < 0.0005) and RMSD (-0.4, p-values < 0.005). Although CATH and SCOP classifications are different, they perform similarly. Moreover, proteins from all structural classes benefit from the proposed methodology. Further analysis also shows that methods relying on class-based fragments produce conformations which are more relevant to user and converge quicker towards the best model as estimated by GDT_TS (up to 10% in average). This substantiates our hypothesis that usage of structurally relevant templates conducts to not only reducing the size of the conformation space to be explored, but also focusing on a more relevant area. CONCLUSIONS: Since our methodology produces models the quality of which is up to 7% higher in average than those generated by a standard fragment-based predictor, we believe it should be considered before conducting any fragment-based protein structure prediction. Despite such progress, ab initio prediction remains a challenging task, especially for proteins of average and large sizes. Apart from improving search strategies and energy functions, integration of additional constraints seems a promising route, especially if they can be accurately predicted from sequence alone.

Generalized Laplacian eigenmaps for modeling and tracking human motions.

This paper presents generalized Laplacian eigenmaps, a novel dimensionality reduction approach designed to address stylistic variations in time series. It generates compact and coherent continuous spaces whose geometry is data-driven. This paper also introduces graph-based particle filter, a novel methodology conceived for efficient tracking in low dimensional space derived from a spectral dimensionality reduction method. Its strengths are a propagation scheme, which facilitates the prediction in time and style, and a noise model coherent with the manifold, which prevents divergence, and increases robustness. Experiments show that a combination of both techniques achieves state-of-the-art performance for human pose tracking in underconstrained scenarios.

Scoring docking conformations using predicted protein interfaces.

BACKGROUND: Since proteins function by interacting with other molecules, analysis of protein-protein interactions is essential for comprehending biological processes. Whereas understanding of atomic interactions within a complex is especially useful for drug design, limitations of experimental techniques have restricted their practical use. Despite progress in docking predictions, there is still room for improvement. In this study, we contribute to this topic by proposing T-PioDock, a framework for detection of a native-like docked complex 3D structure. T-PioDock supports the identification of near-native conformations from 3D models that docking software produced by scoring those models using binding interfaces predicted by the interface predictor, Template based Protein Interface Prediction (T-PIP). RESULTS: First, exhaustive evaluation of interface predictors demonstrates that T-PIP, whose predictions are customised to target complexity, is a state-of-the-art method. Second, comparative study between T-PioDock and other state-of-the-art scoring methods establishes T-PioDock as the best performing approach. Moreover, there is good correlation between T-PioDock performance and quality of docking models, which suggests that progress in docking will lead to even better results at recognising near-native conformations. CONCLUSION: Accurate identification of near-native conformations remains a challenging task. Although availability of 3D complexes will benefit from template-based methods such as T-PioDock, we have identified specific limitations which need to be addressed. First, docking software are still not able to produce native like models for every target. Second, current interface predictors do not explicitly consider pairwise residue interactions between proteins and their interacting partners which leaves ambiguity when assessing quality of complex conformations.

Identification of NAD(P)H quinone oxidoreductase activity in azoreductases from P. aeruginosa: azoreductases and NAD(P)H quinone oxidoreductases belong to the same FMN-dependent superfamily of enzymes.

Water soluble quinones are a group of cytotoxic anti-bacterial compounds that are secreted by many species of plants, invertebrates, fungi and bacteria. Studies in a number of species have shown the importance of quinones in response to pathogenic bacteria of the genus Pseudomonas. Two electron reduction is an important mechanism of quinone detoxification as it generates the less toxic quinol. In most organisms this reaction is carried out by a group of flavoenzymes known as NAD(P)H quinone oxidoreductases. Azoreductases have previously been separate from this group, however using azoreductases from Pseudomonas aeruginosa we show that they can rapidly reduce quinones. Azoreductases from the same organism are also shown to have distinct substrate specificity profiles allowing them to reduce a wide range of quinones. The azoreductase family is also shown to be more extensive than originally thought, due to the large sequence divergence amongst its members. As both NAD(P)H quinone oxidoreductases and azoreductases have related reaction mechanisms it is proposed that they form an enzyme superfamily. The ubiquitous and diverse nature of azoreductases alongside their broad substrate specificity, indicates they play a wide role in cellular survival under adverse conditions.

Episodic reasoning for vision-based human action recognition.

Smart Spaces, Ambient Intelligence, and Ambient Assisted Living are environmental paradigms that strongly depend on their capability to recognize human actions. While most solutions rest on sensor value interpretations and video analysis applications, few have realized the importance of incorporating common-sense capabilities to support the recognition process. Unfortunately, human action recognition cannot be successfully accomplished by only analyzing body postures. On the contrary, this task should be supported by profound knowledge of human agency nature and its tight connection to the reasons and motivations that explain it. The combination of this knowledge and the knowledge about how the world works is essential for recognizing and understanding human actions without committing common-senseless mistakes. This work demonstrates the impact that episodic reasoning has in improving the accuracy of a computer vision system for human action recognition. This work also presents formalization, implementation, and evaluation details of the knowledge model that supports the episodic reasoning.